The distributed active power control problem is explored by equating wind turbines to multi-agent systems in this *** time delays and unknown topological relations are considered in the proposed *** the graph discover...
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The distributed active power control problem is explored by equating wind turbines to multi-agent systems in this *** time delays and unknown topological relations are considered in the proposed *** the graph discovery algorithm,the algebraic connectivity of the graph is found in ***,a proportional control protocol is proposed based on the adjustable margin of different wind ***,the proposed distributed controller not only handles the problem of supply-demand balance between the wind farm and power grid but also regulates the output power of individual wind turbines based on their ***,simulations are performed on the wind turbines to illustrate the validity of the proposed method.
This paper deals with a cooperation communication problem (relay selection and power control) for mobile underwater acoustic communication networks. To achieve satisfactory transmission capacity, we propose a reinforc...
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This paper deals with a cooperation communication problem (relay selection and power control) for mobile underwater acoustic communication networks. To achieve satisfactory transmission capacity, we propose a reinforcement-learning-based cooperation communication scheme to efficiently resist the highly dynamic communication links and strongly unknown time-varying channel states caused by the mobility of Autonomous Underwater Vehicles (AUVs). Firstly, a particular Markov decision process is developed to model the dynamic relay selection process of mobile AUV in the unknown scenario. In the developed model, an experimental statistical-based partition mechanism is proposed to cope with the greatly increasing dimension of the state space caused by the mobility of AUV, reducing the search optimization difficulty. Secondly, a dual-thread reinforcement learning structure with actual and virtual learning threads is proposed to efficiently track the superior relay action. In the actual learning thread, the proposed improved probability greedy policy enables the AUV to strengthen the exploration for the reward information of potential superior relays on the current state. Meanwhile, in the virtual learning thread, the proposed upper-confidence-bound-index-based uncertainty estimation method can estimate the action-reward level of historical states. Consequently, the combination of actual and virtual learning threads can efficiently obtain satisfactory Q value information, thereby making superior relay decision-making in a short time. Thirdly, a power control mechanism is proposed to reuse the current observed action-reward information and transform the multiple unknown parameter nonlinear joint power optimization problem into a convex optimization problem, thereby enhancing network transmission capacity. Finally, simulation results verify the effectiveness of the proposed scheme. IEEE
Benefiting from the development of hyperspectral imaging technology,hyperspectral image(HSI)classification has become a valuable direction in remote sensing image ***,researchers have found a connection between convol...
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Benefiting from the development of hyperspectral imaging technology,hyperspectral image(HSI)classification has become a valuable direction in remote sensing image ***,researchers have found a connection between convolutional neural networks(CNNs)and Gabor ***,some Gabor-based CNN methods have been proposed for HSI ***,most Gabor-based CNN methods still manually generate Gabor filters whose parameters are empirically set and remain unchanged during the CNN learning ***,these methods require patch cubes as network *** patch cubes may contain interference pixels,which will negatively affect the classification *** address these problems,in this paper,we propose a learnable three-dimensional(3D)Gabor convolutional network with global affinity attention for HSI *** precisely,the learnable 3D Gabor convolution kernel is constructed by the 3D Gabor filter,which can be learned and updated during the training ***,spatial and spectral global affinity attention modules are introduced to capture more discriminative features between spatial locations and spectral bands in the patch cube,thus alleviating the interfering pixels *** results on three well-known HSI datasets(including two natural crop scenarios and one urban scenario)have demonstrated that the proposed network can achieve powerful classification performance and outperforms widely used machine-learning-based and deep-learning-based methods.
The research proposes the application of Digital Intelligent Assistants (DIAs) as proactive agents that can support employees in dealing with cybersecurity issues in sustainable industrial processes underlying the imp...
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Grasp detection is a visual recognition task where the robot makes use of its sensors to detect graspable objects in its *** the steady progress in robotic grasping,it is still difficult to achieve both real-time and ...
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Grasp detection is a visual recognition task where the robot makes use of its sensors to detect graspable objects in its *** the steady progress in robotic grasping,it is still difficult to achieve both real-time and high accuracy grasping *** this paper,we propose a real-time robotic grasp detection method,which can accurately predict potential grasp for parallel-plate robotic grippers using RGB *** work employs an end-to-end convolutional neural network which consists of a feature descriptor and a grasp *** for the first time,we add an attention mechanism to the grasp detection task,which enables the network to focus on grasp regions rather than ***,we present an angular label smoothing strategy in our grasp detection method to enhance the fault tolerance of the *** quantitatively and qualitatively evaluate our grasp detection method from different aspects on the public Cornell dataset and Jacquard *** experiments demonstrate that our grasp detection method achieves superior performance to the state-of-the-art *** particular,our grasp detection method ranked first on both the Cornell dataset and the Jacquard dataset,giving rise to the accuracy of 98.9%and 95.6%,respectively at realtime calculation speed.
With the rise in frequency of catastrophic events, enviromental protection and risk management have become critical challenges for assuring both the safety of human pop-ulations and the sustainability of ecosystems. I...
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One of the great concerns when tackling nonlinear systems is how to design a robust controller that is able to deal with *** researchers have been working on developing such type of *** of the most effi-cient technique...
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One of the great concerns when tackling nonlinear systems is how to design a robust controller that is able to deal with *** researchers have been working on developing such type of *** of the most effi-cient techniques employed to develop such controllers is sliding mode control(SMC).However,the low order SMC suffers from chattering problem which harm the actuators of the control system and thus unsuitable to be used in many practical *** this paper,the drawbacks of low order traditional sliding mode control(FOTSMC)are resolved by presenting a novel adaptive radial basis function neural network–based generalized rth order sliding mode control strategy for nth order uncertain nonlinear *** proposed solution adopts neural networks for their excellent capability in function approximation and thus used to approximate the nonlinearities and uncertainties for systems under *** approximation errors are completely considered in the developed *** proposed approach can be used with any order of sliding mode and thus can be generally used with various types of *** global sta-bility of the proposed control approach is proved through Lyapunov stability *** proposed approach is validated and assessed through simulations on the nonlinear inverted pendulum system with severe modeling *** simulations results show that the proposed approach provide superior perfor-mance compared with other approaches in the literature.
Dear Editor,This letter proposes a contrastive consensus graph learning model for multi-view *** are usually built to outline the correlation between multi-model objects in clustering task,and multiview graph clusteri...
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Dear Editor,This letter proposes a contrastive consensus graph learning model for multi-view *** are usually built to outline the correlation between multi-model objects in clustering task,and multiview graph clustering aims to learn a consensus graph that integrates the spatial property of each view.
Querying power big data across multiple sections plays an important role in achieving efficient power data sharing and emergent accident handling. However, power big data often suffers from the issues of data sparsity...
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Due to the complex flow state of pneumatically conveyed particles and the influence of the conveying conditions, existing measurement techniques have limitations in detecting the dynamic parameters of full-sections pa...
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